Metainference: A Bayesian inference method for heterogeneous systems.
Metainference: A Bayesian inference method for heterogeneous systems.
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DOI：
10.1126/sciadv.1501177
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发表时间：
2016-01
期刊：
影响因子：
13.6
通讯作者：
Vendruscolo M
中科院分区：
文献类型：
--
作者：
Bonomi M;Camilloni C;Cavalli A;Vendruscolo M
研究人员提出了一种用于异构系统的贝叶斯推理方法，该方法将先验信息与噪声实验数据相结合。对复杂系统进行建模几乎总是一项具有挑战性的任务。实验观测的结合可以用来提高模型的质量，从而获得有关相应系统行为的更好预测。然而，这种方法受到各种不同误差的影响，特别是当系统同时填充不同状态的集合并且实验数据被测量为这些状态的平均值时。为了解决这个问题，我们提出了一个贝叶斯推理方法，称为“元推理”，这是能够处理的错误，在实验测量和实验测量平均在多个国家。为了实现这一目标，元推理使用复制方法对模型分布的有限样本进行建模，这是基于最大熵原理的复制平均建模的精神。为了说明该方法，我们提出了它的应用程序的异构模型系统和确定的合奏结构对应的蛋白质分子的热波动。因此，Metainference提供了一种方法来建模复杂的系统与异构组件和不同的状态之间的相互转换，考虑到所有可能的错误来源。
Researchers present a Bayesian inference method for heterogeneous systems that integrates prior information with noisy experimental data. Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called “metainference,” that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors.